LSCMNet: A Lightweight Segmentation Network Based on Co-Occurring Matrix for Seismic Image

Seismic image segmentation is important in geological interpretation. In recent years, numerous studies have leveraged texture features to analyze seismic images. However, traditional texture feature extraction methods are computationally intensive and cannot be updated through back-propagation. To address these challenges, we propose a model named Lightweight Segmentation Network based on Co-occurring Matrix (LSCM-Net). The overall architecture of LSCMNet employs an asymmetric encoder-decoder structure. The encoder mainly consists of a lightweight bottleneck that integrates the Parametric Co-occurrence Matrix model based on the Convolutional Neural Network (CNN) for Segmentation (S-PCMCNN) module, along with channel shuffle and split for feature fusion, enhancing the model representational capacity. The pyramid decoder encompasses a spatial attention mechanism. This design significantly reduces the parameters while maintaining accuracy in seismic image segmentation. In the application of igneous rocks, an ablation experiment was conducted to validate the effectiveness of the S-PCMCNN module. Moreover, compared with other classical segmentation models, LSCMNet demonstrates superior segmentation accuracy in few-shot scenarios while having fewer parameters and floating point operations (FLOPs).